Proceedings of the 2nd International Workshop on Social Sensing最新文献_第2页

Spatio-Temporal Modeling of Criminal Activity 犯罪活动的时空建模

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055613

Michail Misyrlis, C. Cheung, Ajitesh Srivastava, R. Kannan, V. Prasanna

引用次数: 6

From Ideas to Social Signals: Spatiotemporal Analysis of Social Media Dynamics 从观念到社会信号:社会媒体动态的时空分析

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055609

Filipe Condessa, R. Marculescu

引用次数: 2

Exploring Compliance: Observations from a Large Scale Fitbit Study 探索依从性:来自大规模Fitbit研究的观察结果

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055608

Louis Faust, Rachael Purta, David S. Hachen, A. Striegel, C. Poellabauer, Omar Lizardo, N. Chawla

引用次数: 21

Privacy Mindset for Developing Internet of Things Applications for Social Sensing: Software Engineering Challenges 为社会感知开发物联网应用的隐私思维:软件工程挑战

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055621

Charith Perera, A. Vasilakos

引用次数: 0

Mining Mobile Sensor Data for Social Behaviors 挖掘移动传感器数据的社会行为

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055620

Huan Li, Yuanjie Gou

引用次数: 1

Identifying the Space Buddies to Track Lost Items 识别太空伙伴追踪丢失物品

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055611

E. Bulut, B. Szymanski

{"title":"Identifying the Space Buddies to Track Lost Items","authors":"E. Bulut, B. Szymanski","doi":"10.1145/3055601.3055611","DOIUrl":"https://doi.org/10.1145/3055601.3055611","url":null,"abstract":"Locating missing or lost objects has always been a challenging task. RFID technology and participatory sensing based approaches have offered solutions but often their adoption was limited due to the high hardware costs or low active participation problem. With the introduction of iBeacon technology and smartphones having BLE capability, tracking such objects has become easier and cost-effective. Objects of care are labeled by attaching to them affordable iBeacon tags, and smartphones in the proximity of these tags sense their presence opportunistically through the applications running in the background. In this paper, we study the tracking of lost objects through the collaboration among users. We analyze the visit patterns of users at the same locations and develop a metric that quantifies for each user the potential benefit of others in terms of their capability of finding that user's lost objects. Depending on the predicted benefits, each user's preference list of other users is formed and then utilized to identify the space buddies who can best track her lost items. The identification is based on the adaption of the solution to the roommate matching problem. We apply the proposed system to two different location based social network datasets and show its effectiveness in different settings.","PeriodicalId":360957,"journal":{"name":"Proceedings of the 2nd International Workshop on Social Sensing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114719859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Graph-Theoretic Approach for Increasing Participation in Social Sensing 提高社会感知参与的图论方法

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055610

W. Abbas, Aron Laszka, X. Koutsoukos

{"title":"Graph-Theoretic Approach for Increasing Participation in Social Sensing","authors":"W. Abbas, Aron Laszka, X. Koutsoukos","doi":"10.1145/3055601.3055610","DOIUrl":"https://doi.org/10.1145/3055601.3055610","url":null,"abstract":"Participatory sensing enables individuals, each with limited sensing capability, to share measurements and contribute towards developing a complete knowledge of their environment. The success of a participatory sensing application is often measured in terms of the number of users participating. In most cases, an individual's eagerness to participate depends on the group of users who already participate. For instance, when users share data with their peers in a social network, the engagement of an individual depends on its peers. Such engagement rules have been studied in the context of social networks using the concept of k-core, which assumes that participation is determined solely by network topology. However, in participatory sensing, engagement rules must also consider user heterogeneity, such as differences in sensing capabilities and physical location. To account for heterogeneity, we introduce the concept of (r, s)-core to model the set of participating users. We formulate the problem of maximizing the size of the (r, s)-core using 1) anchor users, who are incentivized to participate regardless of their peers, and by 2) assigning capabilities to users. Since these problems are computationally challenging, we study heuristic algorithms for solving them. Based on real-world social networks as well as random graphs, we provide numerical results showing significant improvement compared to random selection of anchor nodes and label assignments.","PeriodicalId":360957,"journal":{"name":"Proceedings of the 2nd International Workshop on Social Sensing","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133694190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Machine Learning Approach to Demographic Prediction using Geohashes 使用地理哈希进行人口预测的机器学习方法

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-18 DOI: 10.1145/3055601.3055603

Avipsa Roy, E. Pebesma

{"title":"A Machine Learning Approach to Demographic Prediction using Geohashes","authors":"Avipsa Roy, E. Pebesma","doi":"10.1145/3055601.3055603","DOIUrl":"https://doi.org/10.1145/3055601.3055603","url":null,"abstract":"With the rapid proliferation of smartphones, human beings act as social sensors by means of carrying GPS-enabled devices that share location data. This has resulted in an abundance of sensor data gathered over long periods of time. Gaining meaningful insights from such massive amounts of spatio-temporal data accumulated by several disparate sources is often a challenge for organizations. Identifying demographics of mobile phone users by telecommunication providers is one such example. Demographic information plays a very significant role in targeting online advertisements to focused user groups by gaining insights about userfis mobility patterns. However, in practice, demographic information such as age and gender are mostly unavailable to app developers for open access due to privacy concerns. In this paper, we try to address the gap of how to enrich location data with demographics, which could be valuable for app developers. In our study, we use a machine learning approach to predict the gender and age of mobile phone users from a set of 3,252,950 anonymised GPS trajectories with 60,865 unique devices using a predictive model which is based upon the concept of Geohashes. We study to what extent usersfi demographics could be inferred from their frequently visited locations by encoding by formulating a multi-level classification algorithm to find the most frequently visited Geohashes and associating them with nearest points of interests which would enable predicting age-group and gender of the users who prefer to visit a specific location in a sequential manner. Experiments are conducted on a real dataset of mobile phone users collected and shared by a telecommunication provider. Th The experimental results show that the proposed algorithm can achieve mean prediction accuracy scores of 71.62% and 96.75% for predicting gender and age groups of the users respectively.","PeriodicalId":360957,"journal":{"name":"Proceedings of the 2nd International Workshop on Social Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114569955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance? 来自众包的信心报告是否有利于众包绩效?

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-04-03 DOI: 10.1145/3055601.3055607

Qunwei Li, P. Varshney

引用次数: 11

The Fog Makes Sense: Enabling Social Sensing Services with Limited Internet Connectivity 雾有意义:在有限的互联网连接下实现社会传感服务

Proceedings of the 2nd International Workshop on Social Sensing Pub Date : 2017-03-06 DOI: 10.1145/3055601.3055614

R. Mayer, Harshit Gupta, Enrique Saurez, U. Ramachandran

引用次数: 19